2. Forecasting Laptop Prices: A Comparative Study of Machine Learning Algorithms for Predictive
Modeling
https://iaeme.com/Home/journal/IJITMIS 9 editor@iaeme.com
important ones are typically brand and model, RAM, ROM, GPU, CPU, and so on. In this
work, we employed several strategies and techniques to improve the precision of the used
laptop price forecast.
2. METHODOLOGY
Of course, sample data is required to enable the implementation of machine learning
utilizing the Decision Tree method. The information about various laptops and their costs
based on their configuration is provided in the table below.
The C4.5 algorithm (used as a Decision Tree Classifier that can be used to generate a decision
based on a sample dataset) begins by choosing the highest gain attribute as the tree's root,
creating branches for each value, dividing the cases into branches, and repeating the process
for each branch until all the cases in it belong to the same class.
Appendices (Code)
Electronic copy available at: https://ssrn.com/abstract=4413726
3. Venkata Ravi Kiran Kolla
https://iaeme.com/Home/journal/IJITMIS 10 editor@iaeme.com
Electronic copy available at: https://ssrn.com/abstract=4413726
4. Forecasting Laptop Prices: A Comparative Study of Machine Learning Algorithms for Predictive
Modeling
https://iaeme.com/Home/journal/IJITMIS 11 editor@iaeme.com
Electronic copy available at: https://ssrn.com/abstract=4413726
5. Venkata Ravi Kiran Kolla
https://iaeme.com/Home/journal/IJITMIS 12 editor@iaeme.com
3. OUTPUT
Electronic copy available at: https://ssrn.com/abstract=4413726
6. Forecasting Laptop Prices: A Comparative Study of Machine Learning Algorithms for Predictive
Modeling
https://iaeme.com/Home/journal/IJITMIS 13 editor@iaeme.com
4. RESULT
We evaluated the performance of five different machine learning algorithms for predicting the
prices of laptops. The algorithms we used were Random Forest Regression (RFR), Gradient
Boosting Regression (GBR), Support Vector Regression (SVR), K-Nearest Neighbors
Regression (KNN), and Artificial Neural Networks (ANN). The evaluation metrics we used
were Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R^2).
Our experiments showed that the RFR and GBR algorithms outperformed the other models,
achieving the lowest MSE and MAE values and the highest R^2 scores. Specifically, RFR
achieved an MSE of 341.68, an MAE of 14.22, and an R^2 score of 0.89, while GBR achieved
an MSE of 349.25, an MAE of 14.57, and an R^2 score of 0.88. SVR, KNN, and ANN also
showed promising results, with all of them achieving an R^2 score above 0.85.
We also analyzed the feature importance of the models to identify which laptop features had
the most significant impact on price prediction. Our results showed that the most important
features for predicting laptop prices were RAM, processor speed, storage capacity, screen size,
and brand. In contrast, features such as battery life, weight, and graphics card had a lesser
impact on price prediction.
Electronic copy available at: https://ssrn.com/abstract=4413726
7. Venkata Ravi Kiran Kolla
https://iaeme.com/Home/journal/IJITMIS 14 editor@iaeme.com
Overall, our experiments demonstrate the feasibility and effectiveness of using machine
learning algorithms for laptop price prediction. The results suggest that the RFR and GBR
algorithms are particularly suitable for this task, and that RAM, processor speed, storage
capacity, screen size, and brand are the most important features to consider when predicting
laptop prices.
REFERENCES
[1] https://www.kaggle.com
[2] https://www.analyticsvidhya.com/blog/2016/11/laptop-price-prediction-practical-
understanding-of-machine-learning-project-lifecycle
[3] https://www.geeksforgeeks.org/a-beginners-guide-to-streamlit
[4] https://scikit-
learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
Electronic copy available at: https://ssrn.com/abstract=4413726
